The Inconvenient Truth About Data Science

By Kamil Bartocha (lastminute.com)

1. Data is never clean.
   
   
2. You will spend most of your time cleaning and preparing data.
   
   
3. 95% of tasks do not require deep learning.
   
   
4. In 90% of cases generalized linear regression will do the trick.
   
   
5. Big Data is just a tool.
   
   
6. You should embrace the Bayesian approach.
   
   
7. No one cares how you did it.
   
   
8. Academia and business are two different worlds.
   
   
9. Presentation is key - be a master of Power Point.
   
   
10. All models are false, but some are useful.
    
    
11. There is no fully automated Data Science. You need to get your hands dirty.
    
    

Bio: Kamil Bartocha is Head of Data Science at lastminute.com, and an expert in the field of data processing, data systems architecture and artificial intelligence.

Original.

(Editor: What are your inconvenient truths? Please comment)

Related:

• Interview: Alessandro Gagliardi, Glassdoor on the Fun and Boring Part of Data Scientist Job
• Automatic Statistician and the Profoundly Desired Automation for Data Science
• A Data Scientist Advice to Business Schools